Mobile crowdsensing (MCS) is a new and promising paradigm of data collection due to the growing number of mobile smart devices. It can be utilized in applications of large-scale sensing by employing a group of mobile users with their smart devices. Since a large number of mobile users are recruited, the allocation of sensing tasks to mobile users has a critical influence on the performance of MCS applications. To efficiently assign sensing tasks to mobile users, we propose a novel MCS framework named DeepSensing. This framework consists of six executive phases, i.e., registration of sensing tasks, the announcement of reward rule, collection of users’ information, task allocation, execution of sensing activities, and distribution of data and rewards. Here, the phase of task allocation is a key component, which directly determines the performance of DeepSensing, e.g., the platform’s profit. DeepSensing aims to maximize the platform’s profit by taking into account the various constraints of sensing tasks and mobile users. Therefore, we propose a deep reinforcement learning (DRL) method to optimally assign sensing tasks to mobile users. Specifically, we employ a double deep $Q$ -network with prioritized experience replay (DDQN-PER) to address the task allocation problem, which is also formulated as a path planning problem with time windows. To evaluate our proposed DDQN-PER solution, three baseline solutions are provided, i.e., the ant colony system (ACS), $\epsilon $ -greedy, and random solutions. Finally, the results of numerical simulations show that our proposed DDQN-PER solution outperforms the baseline solutions in terms of the platform’s profit and it plans better organized traveling paths for mobile users.

中文翻译：

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DeepSensing：具有双深度的新型移动人群拥挤框架 问-网络和优先体验重播

由于移动智能设备的数量不断增加，移动人群感知（MCS）是一种新的，很有前途的数据收集范例。通过将一组移动用户及其智能设备一起使用，可以将其用于大规模感测应用。由于招募了大量移动用户，因此将传感任务分配给移动用户对MCS应用程序的性能具有至关重要的影响。为了有效地将感测任务分配给移动用户，我们提出了一种名为DeepSensing的新型MCS框架。该框架包括六个执行阶段，即感测任务的注册，奖励规则的公告，用户信息的收集，任务分配，感测活动的执行以及数据和奖励的分配。在这里，任务分配阶段是关键组成部分，这直接决定了DeepSensing的性能，例如平台的利润。DeepSensing的目的是通过考虑传感任务和移动用户的各种限制，最大限度地提高平台的利润。因此，我们提出了一种深度强化学习（DRL）方法，以将感测任务最佳地分配给移动用户。具体来说，我们采用双倍深度 $ Q $ -具有优先经验回放的网络（DDQN-PER）以解决任务分配问题，该问题也被表述为具有时间窗口的路径规划问题。为了评估我们提出的DDQN-PER解决方案，提供了三种基准解决方案，即蚁群系统（ACS）， $ \ epsilon $ -贪婪和随机解决方案。最后，数值模拟结果表明，我们提出的DDQN-PER解决方案在平台利润方面胜过了基线解决方案，并且为移动用户规划了更好的组织性旅行路径。